Deciphering Physiological and Pathological Influences on Amygdala-Hippocampus Connectivity

Temporal lobe epilepsy (TLE) is associated with disrupted functional integrity in the amygdala-hippocampus complex. Cortico-cortical evoked potentials (CCEPs) can characterize this disruption and have been proposed as biomarkers of the epileptogenic zone (EZ), but their study is typically limited by the spatial sampling bias inherent to whole-brain intracranial EEG. We investigated how epileptogenicity shapes effective connectivity in the amygdala-hippocampus complex, whether structural connectivity underlies it, and ultimately derived a multimodal EZ biomarker.

We retrospectively included 71 patients (50 adults, 21 children) who underwent single-pulse electrical stimulation protocols with intracranial contacts in the amygdala or hippocampus; 15 also underwent diffusion MRI. CCEPs were visually detected, and the latency and amplitude of the first response peak (D1) were extracted. Structural connectivity metrics (tract length, quantitative and fractional anisotropy, mean diffusivity) were derived between the same contacts. A Bayesian linear mixed model (BLMM) related D1 latency to clinical, neurophysiological, and structural predictors, handling missing DTI values jointly within the model. A corrected latency score was then built to discriminate epileptogenic from non-epileptogenic contacts.

Among 6257 possible stimulation-recording pairs, 1027 CCEPs were detected with a significantly higher rate in the hippocampus compared to the amygdala. The BLMM identified robust associations between D1 latency and epileptogenicity, epilepsy type, ipsilateral stimulation, stimulation site (hippocampus/amygdala), and quantitative and fractional anisotropy. The resulting EZ score, obtained by extracting the EZ term's contribution from the BLMM equation, demonstrated an ability to discriminate epileptogenic contacts, showing a balanced accuracy of 78% (sensitivity 86%, specificity 71%), and the resulting EZ probability, based on an elastic net logistic regression, showed a balanced accuracy of 84% (sensitivity 86%, specificity 82%).

These findings suggest that effective connectivity results from the interplay of opposing physiological (here amygdala vs. hippocampus) and pathological (epilepsy-related) influences rather than a simple facilitation within the EZ, and that white matter microstructure independently contributes to this timing. Connectivity is slower within the EZ itself, with an even greater delay observed in its vicinity compared to other brain areas. The resulting EZ score offers a practical, closed-form tool to strengthen EZ localization, and paves the way toward a structurally informed, CCEP-based framework extendable to other brain regions.